1,404 research outputs found
The brown algal genus Fucus : A unique insight into reproduction and the evolution of sex-biased genes
Doctoral thesis (PhD) - Nord University, 2023publishedVersio
Restoring and valuing global kelp forest ecosystems
Kelp forests cover ~30% of the worldâs coastline and are the largest biogenic marine habitat on earth. Across their distribution, kelp forests are essential for the healthy functioning of marine ecosystems and consequently underpin many of the benefits coastal societies receive from the ocean. Concurrently, rising sea temperatures, overgrazing by marine herbivores, sedimentation, and water pollution have caused kelp forests populations to decline in most regions across the world. Effectively managing the response to these declines will be pivotal to maintaining healthy marine ecosystems and ensuring the benefits they provide are equitably distributed to coastal societies.
In Chapter 1, I review how the marine management paradigm has shifted from protection to restoration as well as the consequences of this shift. Chapter 2 introduces the field of kelp forest restoration and provides a quantitative and qualitative review of 300 years of kelp forest restoration, exploring the genesis of restoration efforts, the lessons we have learned about restoration, and how we can develop the field for the future. Chapter 3 is a direct answer to the question faced while completing Chapter 2. This chapter details the need for a standardized marine restoration reporting framework, the benefits that it would provide, the challenges presented by creating one, and the solutions to these problems. Similarly, Chapter 4 is a response to the gaps discovered in Chapter 2. Chapter 4 explores how we can use naturally occurring positive species interactions and synergies with human activities to not only increase the benefits from ecosystem restoration but increase the probability that restoration is successful. The decision to restore an ecosystem or not is informed by the values and priorities of the society living in or managing that ecosystem. Chapter 5 quantifies the fisheries production, nutrient cycling, and carbon sequestration potential of five key genera of globally distributed kelp forests.
I conclude the thesis by reviewing the lessons learned and the steps required to advance the field kelp forest restoration and conservation
Molecular signals of arms race evolution between RNA viruses and their hosts
Viruses are intracellular parasites that hijack their hostsâ cellular machinery to replicate themselves. This creates an evolutionary âarms raceâ between hosts and viruses, where the former develop mechanisms to restrict viral infection and the latter evolve ways to circumvent these molecular barriers. In this thesis, I explore examples of this virus-host molecular interplay, focusing on events in the evolutionary histories of both viruses and hosts. The thesis begins by examining how recombination, the exchange of genetic material between related viruses, expands the genomic diversity of the Sarbecovirus subgenus, which includes SARS-CoV responsible for the 2002 SARS epidemic and SARS-CoV-2 responsible for the COVID-19 pandemic. On the host side, I examine the evolutionary interaction between RNA viruses and two interferon-stimulated genes expressed in hosts. First, I show how the 2âČ-5âČ-oligoadenylate synthetase 1 (OAS1) gene of horseshoe bats (Rhinolophoidea), the reservoir host of sarbecoviruses, lost its anti-coronaviral activity at the base of this bat superfamily. By reconstructing the Rhinolophoidea common ancestor OAS1 protein, I first validate the loss of antiviral function and highlight the implications of this event in the virus-host association between sarbecoviruses and horseshoe bat hosts. Second, I focus on the evolution of the human butyrophilin subfamily 3 member A3 (BTN3A3) gene which restricts infection by avian influenza A viruses (IAV). The evolutionary analysis reveals that BTN3A3âs anti-IAV function was gained within the primates and that specific amino acid substitutions need to be acquired in IAVsâ NP protein to evade the human BTN3A3 activity. Gain of BTN3A3-evasion-conferring substitutions correlate with all major human IAV pandemics and epidemics, making these NP residues key markers for IAV transmissibility potential to humans. In the final part of the thesis, I present a novel approach for evaluating dinucleotide compositional biases in virus genomes. An application of my metric on the Flaviviridae virus family uncovers how ancestral host shifts of these viruses correlate with adaptive shifts in their genomesâ dinucleotide representation. Collectively, the contents of this thesis extend our understanding of how viruses interact with their hosts along their intertangled evolution and provide insights into virus host switching and pandemic preparedness
An optimal approach to the design of experiments for the automatic characterisation of biosystems
The Design-Build-Test-Learn cycle is the main approach of synthetic biology to re-design and create new biological parts and systems, targeting the solution for complex and challenging paramount problems. The applications of the novel designs range from biosensing and bioremediation of water pollutants (e.g. heavy metals) to drug discovery and delivery (e.g. cancer treatment) or biofuel production (e.g. butanol and ethanol), amongst others. Standardisation, predictability and automation are crucial elements for synthetic biology to efficiently attain these objectives. Mathematical modelling is a powerful tool that allows us to understand, predict, and control these systems, as shown in many other disciplines such as particle physics, chemical engineering, epidemiology and economics. Yet, the inherent difficulties of using mathematical models substantially slowed their adoption by the synthetic biology community.
Researchers might develop different competing model alternatives in absence of in-depth knowledge of a system, consequently being left with the burden of with having to find the best one. Models also come with unknown and difficult to measure parameters that need to be inferred from experimental data. Moreover, the varying informative content of different experiments hampers the solution of these model selection and parameter identification problems, adding to the scarcity and noisiness of laborious-to-obtain data. The difficulty to solve these non-linear optimisation problems limited the widespread use of advantageous mathematical models in synthetic biology, broadening the gap between computational and experimental scientists. In this work, I present the solutions to the problems of parameter identification, model selection and experimental design, validating them with in vivo data. First, I use Bayesian inference to estimate model parameters, relaxing the traditional noise assumptions associated with this problem. I also apply information-theoretic approaches to evaluate the amount of information extracted from experiments (entropy gain). Next, I define methodologies to quantify the informative content of tentative experiments planned for model selection (distance between predictions of competing models) and parameter inference (model prediction uncertainty). Then, I use the two methods to define efficient platforms for optimal experimental design and use a synthetic gene circuit (the genetic toggle switch) to substantiate the results, computationally and experimentally. I also expand strategies to optimally design experiments for parameter identification to update parameter information and input designs during the execution of these (on-line optimal experimental design) using microfluidics. Finally, I developed an open-source and easy-to-use Julia package, BOMBs.jl, automating all the above functionalities to facilitate their dissemination and use amongst the synthetic biology community
Sources and consequences of cell-to-cell variability in gene expression
Gene expression is a stochastic process. A population of genetically identical cells grown in an identical environment can express different amounts of any given gene. This cell-to-cell variability in gene expression often underlies important phenotypic differences between cells. If we understand the sources of cell-to-cell variability in gene expression this can help uncover the mechanisms of transcription. The core of this thesis is split into two chapters related to cell-to-cell variability in gene expression. I first examine the sources and impact of cell-to-cell variability on a specific phenotype, a signaling pathway response. In the next chapter I discuss the results of using a novel technology that I co-developed to uncover the sources of cell-to-cell variability.
In the second chapter, I examine cell-to-cell variability in the Hedgehog signaling pathway response. The Hedgehog signaling pathway is an important developmental pathway. We examined cell-to-cell variability in the timing of genetically identical cells to respond to stimulation of this pathway using an established cell-culture model of hedgehog signaling. We identify two groups of cells in unstimulated cells, one that can respond very fast to hedgehog stimulation and another that responds on a slower timescale. We hypothesized that stochastic fluctuations in transcription factor activity underlie these differences between the two groups of cells. Using computational analysis of single-cell RNA sequencing data, at three different time-points after stimulation, I identified multiple transcription factors that are differentially expressed between the fast responders and the slow responders. Overexpression of four of these transcription factors can partly re-create the fast responder cell-state. I also found that overexpression of one of the transcription factors, Prrx1 is sufficient to drive the fast response. We conclude that stochastic cell-to-cell variability of Prrx1 underlies part of the cell-to-cell variability in how fast a given cell can respond to hedgehog pathway stimulation. An important follow-up question that I tackle in the next chapter is what are the factors that determine the cell-to-cell variability of different genes in the genome.
In the third chapter, I describe a novel technique I co-developed for measuring the factors underlying cell-to-cell variability across the genome. Specifically, I looked at the effect of genomic environments and cellular environments on cell-to-cell variability. Using this method, we integrate the same reporter in multiple genomic locations and measuring the expression of the reporter gene at a single-cell level. This helps us estimate the expression mean and variance of the same reporter gene at multiple genomic locations. Using this method, we also measure the global transcriptome of the same cells expressing the reporter. We then associated the cell-to-cell variability of the reporter at different genomic locations with the chromatin at the different genomic locations to uncover potential relationship between chromatin and cell-to-cell variability. We find that the chromatin marks that are associated with the cell-to-cell variability are often different from or have a different direction of association compared to the chromatin marks that are associated with the mean level of expression. We also used the global transcriptome to understand the effect of cell state on the cell-to-cell variability. Finally, using pairs of reporters observed in different cells we decomposed the total cell-to-cell variability into extrinsic and intrinsic noise and find that the cell-state influences the extrinsic noise.
Cell-to-cell variability in gene expression, amongst genetically identical cells in the same environment can have functional consequences. One such consequence that I detected is the variability in the timing of response to hedgehog stimulation. The sources of cell-to-cell variability are important to understand both to further our fundamental understanding of the mechanisms of transcription and for practical reasons such as genome engineering and gene-therapy efforts. The genomic and cellular environment features associated with cell-to-cell variability uncovered in this thesis are good candidates for further examination of the sources of noise
The compatible solutes ectoine and 5-hydroxyectoine: Catabolism and regulatory mechanisms
To cope with osmotic stress many microorganisms make use of short, osmotically active, organic compounds, the so-called compatible solutes. Examples for especially effective members of this type of molecules are the tetrahydropyrimidines ectoine and 5-hydroxyectoine. Both molecules are produced by a large number of microorganisms, not only to fend-off osmotic stress, but also for example low and high temperature challenges. The biosynthetic pathway used by these organisms to synthesize ectoines has already been studied intensively and the enzymes used therein are characterized quite well, both biochemically as well as structurally. However, synthesis of ectoines is only half the story. Inevitably, ectoines are frequently released from the producer cells in different environmental settings. Especially in highly competitive habitats like the upper ocean layers some bacteria specialized on a niche like this. The model organism used in this work is such a species. It is the marine bacterium Ruegeria pomeroyi DSS-3 which belongs to the Roseobacter-clade. Roseobacter species are heterotrophic Proteobacteria which can live in symbiosis with phytoplankton as well as turning against them in a bacterial warfare fashion to scavenge valuable nutrients. Ectoines can be imported by R. pomeroyi DSS-3 in a high-affinity fashion and be used as energy as well as carbon- and nitrogen-sources. To achieve this, both ectoines rings are degraded by the hydrolase EutD and deacetylated by the deacetylase EutE. The first hydrolysis products α-ADABA (from ectoine) and hydroxy-α-ADABA (from hydroxyectoine) are deacetylated to DABA and hydroxy-DABA which are in additional biochemical reactions transformed to aspartate to fuel the cellâs central metabolism. The role and functioning of the EutDE enzymes which work in a concerted fashion are a central aspect of this work. Both enzymes could be biochemically and structurally characterized, and the architecture of the metabolic pathway could be illuminated. α-ADABA and hydroxy-α-ADABA are not only central to ectoine catabolism, but also to the regulatory mechanisms associated with it. Both molecules serve as inducers of the central regulatory protein of this pathway, the MocR-/GabR-type regulator protein EnuR. In the framework of this dissertation molecular details could be clarified which enable the EnuR repressor molecule to sense both molecules with high affinity to subsequently derepress the genes for the import and catabolism of ectoines
Population analysis of Legionella pneumophila epidemiology and the genetic basis for human pathogenicity
Legionella are globally ubiquitous aquatic bacteria that cause both Pontiac Fever (a mild flu) and Legionnaires' disease, a severe form of pneumonia with a 5-10% mortality rate. They are natural parasites of freshwater protozoa that may also cause opportunistic human infections when inhaled from the environment via aerosols. Human infections are generally sporadic, although the last decade has seen a global increase in the number of infections, and large-scale outbreaks place an appreciable annual burden on public health worldwide. The species Legionella pneumophila causes around 90% of infections, a large number of which are caused by relatively few clonal lineages, each estimated to have emerged recently and independently. However, the factors leading to their pathogenic success still remain largely unknown.
The growing abundance of whole genome sequence (WGS) data has revealed a new horizon for bacterial comparative genomics. Larger, more varied datasets enable more advanced statistical approaches to investigate bacterial evolution, epidemiology and pathogen emergence. In this project, I assembled a comprehensive WGS dataset to conduct population-scale genomic analysis of L. pneumophila. In addition to a historic Scottish reference isolate collection, I downloaded all publically available assemblies and sequence reads for Legionella species. A pipeline was then developed to assemble, filter, clean and curate these data based on a range of parameters, which was improved by visual inspection. I conducted a population-wide meta-analysis of the data to explore the global distribution of Sequence Types (STs) over time. Our results highlight the power of population-scale genomic analysis to monitor disease trends, although several major sources of spatial and temporal sampling bias were identified that should be accounted for in future work.
I then used these data to conduct a nation-wide genomic epidemiological analysis of culture- positive clinical L. pneumophila isolates from Scotland over a 36 year timeframe in context with global isolates and epidemiological metadata. The analysis shed new light on the epidemiology of
travel-associated infections and revealed widely disseminated endemic clones that were associated with repeated infections in Scotland over many years. In addition, specific clones were identified that were isolated from the water systems of individual hospitals over very long time periods, indicating either repeated re-colonisation or long-term environmental persistence. The results indicate that routine regular environmental sampling is required to support the identification of epidemiological links, attribution of outbreak sources and to inform public health measures targeting endemic clones that present an ongoing risk.
Finally, I investigated the genomic features that differentiate clinical and environmental isolates of L. pneumophila and which may be important for human infection potential. I used PIRATE to calculate the L. pneumophila pangenome, which revealed that the number of genes was closely correlated with the population structure, and identified two major lineages in which clinical genomes contained significantly fewer genes. To identify specific genes or variants correlated with an environmental or clinical source, I mapped the hits from a machine learning-based association analysis to corresponding orthologous genes clusters, revealing a number of previously undetected associations with disease. Using a network visualisation approach, I identified strong linkage disequilibrium influencing the significance of hits in commonly syntenic genes throughout the pangenome.
Taken together, the results demonstrate the value of high-resolution population-scale WGS data to monitor the distribution and spread of different Legionella pneumophila clones, including those posing a higher human health risk. Furthermore, it empowered the identification of genomic factors significantly associated with the isolation source, which may contribute towards human infection potential
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